The search and retrieval of digital histopathology slides is an important
task that has yet to be solved. In this case study, we investigate the clinical
readiness of three state-of-the-art histopathology slide search engines,
Yottixel, SISH, and RetCCL, on three patients with solid tumors. We provide a
qualitative assessment of each model's performance in providing retrieval
results that are reliable and useful to pathologists. We found that all three
image search engines fail to produce consistently reliable results and have
difficulties in capturing granular and subtle features of malignancy, limiting
their diagnostic accuracy. Based on our findings, we also propose a minimal set
of requirements to further advance the development of accurate and reliable
histopathology image search engines for successful clinical adoption.

本文通过调查三款最先进的组织病理学幻灯片搜索引擎，对三种实体肿瘤患者的搜索和提取进行了临床评估，发现当前搜索引擎在识别病理学上仍存在困难，因此提出了一些最小化的要求，旨在进一步促进准确和可靠的医学组织病理学图像搜索引擎的发展。

组织病理学切片索引与搜索：已到达目的地吗？

Histopathology Slide Indexing and Search: Are We There Yet?

We present a deep convolutional neural network for breast cancer screening
exam classification, trained and evaluated on over 200,000 exams (over
1,000,000 images). Our network achieves an AUC of 0.895 in predicting whether
there is a cancer in the breast, when tested on the screening population. We
attribute the high accuracy of our model to a two-stage training procedure,
which allows us to use a very high-capacity patch-level network to learn from
pixel-level labels alongside a network learning from macroscopic breast-level
labels. To validate our model, we conducted a reader study with 14 readers,
each reading 720 screening mammogram exams, and find our model to be as
accurate as experienced radiologists when presented with the same data.
Finally, we show that a hybrid model, averaging probability of malignancy
predicted by a radiologist with a prediction of our neural network, is more
accurate than either of the two separately. To better understand our results,
we conduct a thorough analysis of our network's performance on different
subpopulations of the screening population, model design, training procedure,
errors, and properties of its internal representations.

本文提出了一个基于深度卷积神经网络的乳腺癌筛查检查分类方法，并在超过 200,000 个检查（超过 1,000,000 张图像）上进行了训练和评估。该网络在筛查人群中预测乳腺癌的 AUC 为 0.895，其高准确度的原因是采用了两阶段训练程序，并通过可靠的读者研究验证了准确性。最后，研究发现由我们的神经网络预测恶性概率与放射科医生的预测平均值相结合会更加准确。